99 lines
4.8 KiB
Python
99 lines
4.8 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import get_news, get_global_news
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from tradingagents.agents.utils.logging_utils import log_report_preview
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from tradingagents.agents.utils.tool_runner import (
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extract_text_from_content,
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run_chain_with_tools,
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)
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def create_news_analyst(llm):
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def news_analyst_node(state):
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scheduled_list = state.get("scheduled_analysts", []) or []
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scheduled_plan_list = state.get("scheduled_analysts_plan", []) or []
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scheduled_plan = {item.lower() for item in scheduled_plan_list}
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action = state.get("orchestrator_action", "").lower()
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ticker = state.get("target_ticker") or state["company_of_interest"]
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if (scheduled_plan and "news" not in scheduled_plan) or action not in ("", "monitor", "escalate", "trade", "execute"):
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return {
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"news_report": "News analyst skipped by orchestrator directive.",
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"scheduled_analysts": [item for item in scheduled_list if item.lower() != "news"],
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"scheduled_analysts_plan": scheduled_plan_list,
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}
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current_date = state["trade_date"]
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tools = [
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get_news,
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get_global_news,
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]
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system_message = (
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"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. We are looking at the company {ticker}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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chain = prompt | llm.bind_tools(tools)
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try:
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print(f"[News Analyst] Running analysis for {ticker} | scheduled={scheduled_list}")
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except Exception:
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pass
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try:
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result, message_history, tool_runs = run_chain_with_tools(
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chain,
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tools,
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state["messages"],
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)
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except Exception as exc:
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updated_schedule = [item for item in scheduled_list if item.lower() != "news"]
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report = f"News analyst failed: {exc}"
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return {
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"messages": state["messages"],
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"news_report": report,
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"scheduled_analysts": updated_schedule,
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"scheduled_analysts_plan": scheduled_plan_list,
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}
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report = extract_text_from_content(getattr(result, "content", "")) or "News analyst produced no narrative."
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updated_schedule = [item for item in scheduled_list if item.lower() != "news"]
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payload = {
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"messages": message_history,
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"news_report": report,
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"scheduled_analysts": updated_schedule,
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"scheduled_analysts_plan": scheduled_plan_list,
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}
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log_report_preview("News Analyst", ticker, report)
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try:
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print(
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f"[News Analyst] Completed step for {ticker} | tool_runs={tool_runs} | report_len={len(report) if report else 0}"
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)
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except Exception:
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pass
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return payload
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return news_analyst_node
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